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So, congratulations on your new job as a Data Scientist – fresh out of university! You must be super excited to build that game-changing ML model that everyone will be raving about.
But hold up – before you launch into hyperdrive, there are a few key pointers to remember as you embark on your adventure in data science and AI/ML. Read on.
Pure academics is like pure gold. You cannot make much use of pure gold. To make jewelry you need to alloy with other metals. Similarly, pure academic knowledge is not much useful unless mixed with business logic, practical feasibility, and ground-level realities.
There have been several instances where AI/ML models, despite their sophistication, have encountered practical limitations due to a lack of real-world understanding.
One notable example is the case of Microsoft’s AI chatbot “Tay” in 2016. Tay was designed to learn and interact with users on social media platforms, mimicking human conversation. However, within hours of its launch, the bot started producing offensive and inappropriate messages, adopting the negative behaviors it had been exposed to from online users. It was closed down within a few hours of launch!
Tay’s lack of understanding of social and ethical norms in human communication led to its rapid degradation into offensive and harmful interactions. This incident highlighted the challenges of creating business-relevant responsible models that is based on both solid theory and ground realities.
So, don’t jump into building a model unless you understand the whole context. Before diving into model creation, get the bigger picture – why the model is vital and how it aligns to generating meaningful value.
No, I do not mean the famous 80-20 rule which says that roughly 80% of impacts come from 20% of inputs. There’s a different kind of 80-20 rule in the world of data science. See, as a data scientist, your part in a project might make up about 20% of its value. But the whopping 80%? That’s all thanks to teamwork.
Now, don’t get me wrong – your skills, like diving into data and coming up with smart algorithms, are a big deal. But here’s where the magic really happens: when you team up with folks from different areas. Think about it like this – you’ve got the data wizards, the tech experts, the creative minds, and the subject matter experts in whatever field the project is about, all working together. It’s like a team of superheroes joining forces.
Teamwork isn’t just about sharing ideas; it’s about bringing together all these niche skills and experiences together to create something really awesome. Imagine brainstorming sessions where everyone pitches in, debates that spark new ways of thinking, and everyone chipping in to solve problems. Plus, when you’re working together, there’s this energy that comes from knowing you’re all on the same mission.
So, yes, your skills are like the tasty ingredients, but teamwork is what turns them into a delicious dish. As a data scientist, you’re not just crunching numbers; you are part of a team that’s turning data into real-world solutions. And that’s where the real excitement happens. That’s the power of collaboration!
Now, let’s go back to the university days. You were given a fairly complex problem. You were given a reasonable amount of time. And, you were asked to find the best solution.
But now, you’re stepping into the data science practical world, and it’s a whole new ball game.
In this world, problems come at you like rapid-fire challenges. These real-world, industrial puzzles are thrown your way, but the clock’s ticking faster than you can even say the word: “algorithm”! No leisurely strolls. The timeline is critical.
Your solutions? Not only do you need to come up with a feasible solution, but your solution should be practical. You are crafting solutions that companies will invest in. Those will be serving thousands – if not millions of customers. Further, the solutions must be running smoothly and glitch-free.
Well, now in many cases you have to create multiple solutions – like a whole array. Clients want options, like a menu of possibilities, and it’s your job to deliver. It’s a bit like being a chef who whips up a buffet, so the clients can choose what satisfies their appetite best.
So, while university might have been the warm-up, the real practice world is the main event. You’re in the data science spotlight, juggling data, algorithms, and real-world challenges. It’s thrilling because here, you’re not just cracking problems; you’re crafting solutions that keep things ticking and the world spinning.
Now, as a data scientist, your role is to build and deploy long-term sustainable models.
Meanwhile, you might have bagged trophies at a hackathon or a model event. Wonderful and kudos!
But in your job role, you need to go a step further. Unlike a hackathon, your model is not static. It’s a dynamic, ever-shifting event. More like a long marathon.
So, in your role, the data science models aren’t just one-time shows; they’re the engines that keep things going along. Deploying a model isn’t the finish line; it’s more like the starting whistles in a race. Now you’re in for the long haul – a marathon of monitoring, tuning, and constant upgrades.
Think of the dynamic model as your digital pet that needs nurturing and care. Your model’s health depends on your watchful eye and fine-tuning skills. A tweak here, a nudge there – it all adds up to ensure peak performance.
During the process, you might need to wave goodbye to certain features, even if they’re important to your model. Why? Because they might vanish from the scene after a few rounds of the sun (we’re talking variable leakage here).
So, yeah, building models in the industry? It’s a whole different ball game. It’s about creating digital dynamos that thrive on your passion, adaptability, and knack for improvement.
Now let me tell you, the data science role is a highly knowledge-centric role.
You need to soak up knowledge like a sponge.
You might have a specialization in university. Well, here’s the twist – going ultra-niche might lead you down a rabbit hole where the big picture slips through your fingers.
So, here’s the secret sauce – be the jack of all trades and the master of data science. A generalist who has also got a radar for every new buzz in the data science universe. While you’re working on your neural network, keep an eye on the evolving tools, techniques, and algorithms – they’re your toolkit for staying ahead of the curve.
Do not do it just for the sake of doing it. That is the employee mindset. Ask yourself – if this were your own company, would you still do it in the same way? That’s the game-changing ownership mindset that can differentiate you from the rest.
Your journey in data science isn’t just about numbers and algorithms; it’s about owning it, thriving on the big picture, and making every byte count.
For a moment, think of yourself as a master chef in the workplace kitchen! Cook up amazing work dishes each & every time.
Deliver quality results consistently that everyone raves about. Just like how people remember a restaurant for its delicious food, your co-workers and senior bosses will remember you for your top-notch contributions.
Now, play the role of a puzzle solver. Figure out what your company cares about the most. If your company is all about being eco-friendly, and you’re the recycling champ, you’re like the eco-warrior superhero they need.
Match what you’re great at with the company’s goals, vision, and objectives. Soon you will discover your place in the vast organization.
Next, speak up your mind! Share your cool ideas during meetings. If you’re not into live shows, send a message or email later to the host, showing your specific point of views. People will certainly turn back to you, and that’s your brand spotlight.
Finally, spread smiles and high-fives. When you’re the person who brightens up the workplace, you’re building a brand as an awesome and friendly teammate that everyone wants to work with.
Data science is a journey filled with dos and don’ts. It is a path that demands your attention to detail, thirst for knowledge, and knack for collaboration.
Once you embrace those guidelines and make them your own, you will turn into a perfectly alloyed valuable gold!
Your organization isn’t just getting a data scientist; they’re getting a value asset, a problem solver, a team player, and an innovation engine.
All the very best and good luck!
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